BullBear
Stock decision dashboard · AI signals
Opens the live Hugging Face Space.
Open live app ↗BullBear, a graduate financial-analytics course project, ingests historical OHLCV plus real-time Yahoo Finance quotes and produces explainable trading recommendations: a StockSignalAnalyzer computes eight independent signals (RSI, MACD, moving averages, Bollinger Bands, volume, momentum, support/resistance, news sentiment), each with an explicit weight, and combines them into a Buy/Sell/Hold call with a 0–100% confidence score and human-readable reasoning. A Gradio UI sits over a clean core/analysis/visualization architecture using the Strategy pattern to swap technical/fundamental/risk views, computing returns, volatility, Sharpe, max drawdown, VaR and correlation heatmaps over a bundled 20-asset portfolio (2013–2017, 24,520 daily rows). A documented optimization pass (batch quote fetching, caching, adaptive data requirements) cut market-load time from 20+s to ~9s, a 60% speedup. The recommendation engine is deterministic weighted-indicator logic, not a trained model.
- Python
- Gradio
- Pandas
- NumPy
- Plotly
- yahooquery
- SciPy
- Hugging Face Spaces
- Signal engine
- 8 weighted indicators
- Load time
- 60% faster (20s → 9s)
- Bundled OHLCV
- 24,520 rows · 20 assets
- Code
- 11,362 LOC · 17 modules